Techniques to manage channel prediction

ABSTRACT

A system, apparatus, method and article to manage channel prediction for a wireless communication system are described. The apparatus may include a media access control processor to perform channel prediction, and a transceiver to communicate information using the channel prediction. Other embodiments are described and claimed.

CROSS-REFERENCE OF RELATED APPLICATIONS

This application is a continuation of, claims the benefit of andpriority to, previously filed U.S. patent application Ser. No.13/893.236 filed May, 13, 2013 entitled “Techniques to Manage ChannelPrediction”, which is a continuation of U.S. patent application Ser. No.12/908,637 entitled “Techniques for Beamforming Using Predicted ChannelState Information” filed on Oct. 20, 2010, which is a continuation ofU.S. patent application Ser. No. 12/426,716 entitled “Techniques toManage Channel Prediction” filed on Apr. 20, 2009, which is acontinuation of U.S. patent application Ser. No. 11/040,955 entitled“Techniques to Manage Channel Prediction” filed on Jan. 21, 2005, thesubject matter of the above are hereby incorporated by reference intheir entirety.

BACKGROUND

In a wireless communication system, wireless communication devices maytransmit and/or receive radio frequency (RF) signals through one or moreantennas. Some wireless communication devices may attempt to measure thecharacteristics of a communication channel, and modify transmissiontechniques based on the measured results. Techniques to improve suchoperations may improve performance for a wireless communication device,and potentially overall system performance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a system in accordance with oneembodiment.

FIG. 2 illustrates a partial block diagram of a node in accordance withone embodiment.

FIG. 3 illustrates a graph of a time varying fading channel and feedbackdelay in accordance with one embodiment.

FIG. 4 illustrates a graph of a fading magnitude of a predicting filterin accordance with one embodiment.

FIG. 5 illustrates a graph of packet error rates (PER) for systems withand without channel prediction in accordance with one embodiment.

FIG. 6 illustrates a graph of throughputs for systems with and withoutchannel prediction in accordance with one embodiment.

FIG. 7 illustrates a programming logic in accordance with oneembodiment.

DETAILED DESCRIPTION

FIG. 1 illustrates a block diagram of a system 100. System 100 maycomprise, for example, a communication system having multiple nodes. Anode may comprise any physical or logical entity having a unique addressin system 100. Examples of a node may include, but are not necessarilylimited to, a computer, server, workstation, laptop, ultra-laptop,handheld computer, telephone, cellular telephone, personal digitalassistant (PDA), router, switch, bridge, hub, gateway, wireless accesspoint, and so forth. The unique address may comprise, for example, anetwork address such as an Internet Protocol (IP) address, a deviceaddress such as a Media Access Control (MAC) address, and so forth. Theembodiments are not limited in this context.

The nodes of system 100 may be arranged to communicate different typesof information, such as media information and control information. Mediainformation may refer to any data representing content meant for a user,such as voice information, video information, audio information, textinformation, numerical information, alphanumeric symbols, graphics,images, and so forth. Control information may refer to any datarepresenting commands, instructions or control words meant for anautomated system. For example, control information may be used to routemedia information through a system, or instruct a node to process themedia information in a predetermined manner.

The nodes of system 100 may communicate media and control information inaccordance with one or more protocols. A protocol may comprise a set ofpredefined rules or instructions to control how the nodes communicateinformation between each other. The protocol may be defined by one ormore protocol standards as promulgated by a standards organization, suchas the Internet Engineering Task Force (IETF), InternationalTelecommunications Union (ITU), the Institute of Electrical andElectronics Engineers (IEEE), and so forth. For example, system 100 mayoperate in accordance with various wireless local area network (WLAN)protocols, such as the IEEE 802.11, 802.16, and 802.20 series ofstandard protocols. For example, the IEEE 802.16 series of standardprotocols may include the IEEE 802.16-REVd Draft Standard For Local AndMetropolitan Networks titled “Part 16: Air Interface For Fixed BroadbandWireless Access Systems,” May 2004 (“802.16-REVd Draft Standard); andthe IEEE 802.16e Draft Standard For Local And Metropolitan Networkstitled “Part 16: Air Interface For Fixed And Mobile Broadband WirelessAccess Systems, Amendment For Physical And Medium Access Control LayersFor Combined Fixed And Mobile Operation In Licensed Bands,” September2004 (“802.16e Draft Standard”). The embodiments, however, are notlimited in this context.

Referring again to FIG. 1, system 100 may comprise a wirelesscommunication system. In one embodiment, for example, system 100 maycomprise a Broadband Wireless Access (BWA) system operating inaccordance with, for example, the IEEE 802.16 series of standardprotocols, such as the IEEE 802.16-REVd Draft Standard and 802.16e DraftStandard. System 100 may include one or more wireless communicationdevices, such as a base station 110 and subscriber stations 120, 150.The wireless communication devices may all be arranged to communicateinformation signals using wireless shared media 160. Information signalsmay include any type of signal encoded with information, such as mediaand/or control information. Although FIG. 1 is shown with a limitednumber of nodes in a certain topology, it may be appreciated that system100 may include more or less nodes in any type of topology as desiredfor a given implementation. The embodiments are not limited in thiscontext.

In one embodiment, system 100 may include various fixed devices, such asbase station 110. Base station 110 may comprise a generalized equipmentset providing connectivity, management, and control of another device,such as subscriber stations 120, 150. In one embodiment, for example,base station 110 may be implemented as a base station arranged tooperate in accordance with the IEEE 802.16 series of protocols, such asthe IEEE 802.16-REVd Draft Standard, the IEEE 802.16e Draft Standard,and so forth. For example, base station 110 may include a MIMO systemhaving multiple transmitters/receivers (“transceivers”) and multipleantennas. The embodiments are not limited in this context.

In one embodiment, system 100 may include various mobile devices, suchas subscriber stations 120, 150. Subscriber stations 120, 150 maycomprise generalized equipment sets providing connectivity betweensubscriber equipment and another device, such as base station 110, forexample. In one embodiment, for example, subscriber stations 120, 150may be implemented as subscriber stations arranged to operate inaccordance with the IEEE 802.16 series of protocols, such as the IEEE802.16-REVd Draft Standard, the IEEE 802.16e Draft Standard, and soforth. Examples of subscriber stations 120, 150 may include any mobiledevice, such as a mobile or cellular telephone, a computer or laptopequipped with a wireless access card, a handheld device such as awireless PDA with wireless capabilities, an integrated cellulartelephone/PDA, and so forth. The embodiments are not limited in thiscontext.

As with base station 110, subscriber stations 120, 150 may each includea MIMO system having at least two transceivers and two antennas. TheMIMO system, however, may have any number of transceivers and antennas.The embodiments are not limited in this context.

It is worthy to note that although system 100 is shown with wirelesscommunication devices having a MIMO system and multiple antennas, it maybe appreciated that other fixed and mobile devices having only a singletransceiver and antenna may also be modified to utilize the techniquesdescribed herein, and still fall within the scope of the embodiments.The embodiments are not limited in this context.

In general operation, the nodes of system 100 may operate in multipleoperating modes. For example, subscriber station 120, subscriber station150 and base station 110 may operate in at least one of the followingoperating modes: a single-input-single-output (SISO) mode, amultiple-input-single-output (MISO) mode, a single-input-multiple-output(SIMO) mode, and/or in a MIMO mode. In a SISO operating mode, a singletransmitter and a single receiver may be used to communicate informationsignals over a wireless shared medium 160. In a MISO operating mode, twoor more transmitters may transmit information signals over wirelessshared media 160, and information signals may be received from wirelessshared media 160 by a single receiver of a MIMO system. In a SIMOoperating mode, one transmitter and two or more receivers may be used tocommunicate information signals over wireless shared media. In a MIMOoperating mode, two or more transmitters and two or more receivers maybe used to communicate information signals over wireless shared media160.

In one embodiment, one or more nodes of system 100 may use closed loopMIMO techniques. In a closed loop MIMO system, the transmitter typicallyuses channel state information when communicating information over agiven MIMO communication channel. The channel state information mayinclude values representing one or more characteristics of a channel.For example, the channel state information may be used to evaluate thechannel quality or the received signal quality at the receiver aftersome processing, such as spatial demultiplexing. Examples of somechannel characteristics may include signal to noise (SNR),carrier-to-interference-and-noise-ratio (CINR), and so forth. Theparticular number and type of channel characteristics measured for agiven communication channel may vary according to a particularimplementation, and the embodiments are not limited in this context.

In one embodiment, the channel state information may be used for anumber of applications in a communications system to enhance performanceof the system. For example, channel state information may be used inbeamforming for a MIMO transceiver array, selecting an operatingparameter for a system such as modulation coding scheme, and so forth.The type and number of applications are not limited in this context.

In one embodiment, for example, channel state information may allow theMIMO transmitter to use one or more beamforming techniques to improvechannel throughput. A beamforming technique may represent a space-timeprocessing technique directed to improving capacity for a given MIMOchannel, and thereby improving the multiplexing gain. An example of abeamforming technique suitable for use by system 100 may be referred toas eigenbeamforming. Eigenbeamforming may effectively convert a MIMOchannel into a bank of scalar coefficients, thereby reducing oreliminating potential crosstalk between the various scalar channels. Theembodiments are not limited in this context.

Some beamforming techniques, however, may suffer from a problem of amismatch between measured channel state information and actual channelstate information. Depending on the technique used to measure channelstate information, there may be a significant amount of delay betweenthe measured channel state information, and the actual channel stateinformation at the time information is transmitted by the MIMO systemusing the beamforming technique. This delay may be referred to herein as“feedback delay.” On a slowly varying channel, the feedback delay maynot substantially impact performance of the MIMO system. On a fadingchannel, however, the feedback delay may reduce the performance andefficiency of the MIMO system. For example, studies have suggested thatsome MIMO systems using closed loop MIMO techniques may potentially losesome or all performance gains when the speed of a subscriber station is10 kilometers per hour (km/h) or higher. This may occur despite the factthat closed loop MIMO techniques typically provide a 4-5 decibel (dB)gain when compared to open loop MIMO techniques when the speed of asubscriber station is below 3 km/h. Consequently, reducing the impact offeedback delay on a MIMO system may potentially increase performance andefficiency of the MIMO system, particularly for fading channels.

Some embodiments may solve this and other problems. In one embodiment,for example, one or more nodes of system 100 may be arranged to performchannel prediction. More particularly, base station 110 and/orsubscriber stations 120, 150 may be arranged to predict channel stateinformation for a communication channel using previously measuredchannel state information. By predicting channel state information, theimpact of feedback delay between measured channel state information andcurrent channel state information may be reduced. Consequently, atransmitting device may transmit data over a MIMO channel using channelstate information that is more accurate with respect to the actual MIMOchannel state for a given point in time. Accordingly, system 100 mayrealize improved performance and efficiency relative to conventionaltechniques.

FIG. 2 illustrates a partial block diagram of a node 200. Node 200 maybe implemented as part of base station 110, subscriber station 120and/or subscriber station 150 as described with reference to FIG. 1. Asshown in FIG. 2, node 200 may comprise multiple elements, such asprocessor 210, switch (SW) 220, and a transceiver array 230. Someelements may be implemented using, for example, one or more circuits,components, registers, processors, software subroutines, or anycombination thereof. Although FIG. 2 shows a limited number of elements,it can be appreciated that more or less elements may be used in node 200as desired for a given implementation. The embodiments are not limitedin this context.

In one embodiment, node 200 may include a transceiver array 230.Transceiver array 230 may be implemented as, for example, a MIMO system.MIMO system 230 may include two transmitters 240 a and 240 b, and tworeceivers 250 a and 250 b. Although MIMO system 230 is shown with alimited number of transmitters and receivers, it may be appreciated thatMIMO system 230 may include any desired number of transmitters andreceivers. The embodiments are not limited in this context.

In one embodiment, transmitters 240 a-b and receivers 250 a-b of MIMOsystem 230 may be implemented as Orthogonal Frequency DivisionMultiplexing (OFDM) transmitters and receivers. Transmitters 240 a-b andreceivers 250 a-b may communicate data frames with other wirelessdevices. For example, when implemented as part of base station 110,transmitters 240 a-b and receivers 250 a-b may communicate data frameswith subscriber station 120 and subscriber station 150. When implementedas part of subscriber station 120 and/or subscriber station 150,transmitters 240 a-b and receivers 250 a-b may communicate data frameswith base station 110. The data frames may be modulated in accordancewith a number of modulation schemes, to include Binary Phase ShiftKeying (BPSK), Quadrature Phase-Shift Keying (QPSK), QuadratureAmplitude Modulation (QAM), 16-QAM, 64-QAM, and so forth. Theembodiments are not limited in this context.

In one embodiment, transmitter 240 a and receiver 250 a may be operablycoupled to an antenna 260, and transmitter 240 b and receiver 250 b maybe operably coupled to antenna 270. Examples for antenna 260 and/orantenna 270 may include an internal antenna, an omni-directionalantenna, a monopole antenna, a dipole antenna, an end fed antenna or acircularly polarized antenna, a micro-strip antenna, a diversityantenna, a dual antenna, an antenna array, and so forth. The embodimentsare not limited in this context.

In one embodiment, node 200 may include a processor 210. Processor 210may be implemented as a general purpose processor, such as a processormade by Intel® Corporation, for example. Processor 210 may also comprisea dedicated processor, such as a controller, microcontroller, embeddedprocessor, a digital signal processor (DSP), a network processor, aninput/output (I/O) processor, and so forth. The embodiments are notlimited in this context.

In one embodiment, node 200 may include a memory (not shown). The memorymay comprise any machine-readable or computer-readable media capable ofstoring data, including both volatile and non-volatile memory. Forexample, the memory may comprise read-only memory (ROM), random-accessmemory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM),synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM),erasable programmable ROM (EPROM), electrically erasable programmableROM (EEPROM), flash memory, polymer memory such as ferroelectric polymermemory, ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, or any other type of media suitable for storing information. Theembodiments are not limited in this context.

In one embodiment, for example, processor 210 may be arranged to performMAC layer and/or physical (PHY) layer operations. For example, processor210 may be implemented as a media access control (MAC) processor. MAC210 may be arranged to perform MAC layer processing operations. Inaddition, MAC 210 may be arranged to predict channel state informationfor a MIMO channel.

As previously described, node 200 may support closed loop MIMO asdefined by the IEEE 802.16 series of protocols, such as the IEEE802.16-REVd Draft Standard and/or IEEE 802.16e Draft Standard, forexample. In accordance with IEEE 802.16, base station 110 may sound theMIMO channel to measure certain channel characteristics. The channelsounding may be performed using sounding information, such as periodicsounding symbols, preambles, midambles, pilot symbols, and so forth. Asubscriber station (e.g., 120, 150) may feed back the channel stateinformation or beamforming matrices in response to the channel soundingby base station 110 or according to a request from base station 110. Theembodiments are not limited in this context.

In one embodiment, a subscriber station (e.g., 120, 150) may feed backthe channel state information or beamforming matrices for both frequencydivision duplexing (FDD) and time division duplexing (TDD) modes inresponse to the channel sounding by base station 110 or according to arequest from base station 110. For a TDD system exploiting channelreciprocity, the two communicating devices may not necessarily needexplicit channel feedback since they can periodically learn the channelfrom the reverse traffic, assuming the appropriate circuit calibrationare performed and the communication channel changes relatively slowly.This technique is sometimes referred to as “implicit channel feedback.”The embodiments are not limited in this context.

The feedback delay caused by channel sounding operations may besignificant. For example, an 802.16 system may typically have a frameduration of approximately 5 milliseconds (ms). As a result, there may beapproximately 10 ms of feedback delay between the channel sounding andthe actual beamforming used by base station 110. In some instances, thefeedback delay may even be greater than 10 ms for the end part of thebeamformed packet. The channel state, however, may change significantlyduring the feedback delay interval, particularly for mobile channelswhich are prone to fading. This may be described in further detail withreference to FIG. 3.

FIG. 3 illustrates a graph of a time varying fading channel and feedbackdelay in accordance with one embodiment. FIG. 3 illustrates a graphhaving a fading magnitude with respect to time for a fading channel. Thefading channel may be representative of a typical fading channel of amobile device traveling at a speed of approximately 9 km/h and using anoperating frequency band of approximately 2.6 Gigahertz (GHz). Asillustrated by FIG. 3, the fading magnitude of a channel may changesubstantially over time, particularly during a typical feedback delayinterval of 10 ms or more. If a closed-loop MIMO transmitter conductsbeamforming using inaccurate channel state information, the formed beamis not likely to be pointed in the desired direction. Consequently, thefeedback delay may significantly limit the application of closed-loopMIMO to mobile subscribers experiencing time varying fading channels,which is the type of environment for which 802.16e networks arespecifically designed.

In one embodiment, node 200 may be used to measure channel stateinformation for a closed loop MIMO channel formed using wireless sharedmedia 160 between base station 110 and a subscriber station (e.g., 120,150). The channel state information for a given channel may be measuredby multiple nodes within system 100. For example, base station 110 mayinitiate channel sounding operations by sending sounding information ina downlink frame to subscriber station 120. Subscriber station 120 maymeasure one or more characteristics of the channel while receiving thesounding information. Subscriber station 120 may send an uplink framewith the measured channel state information to base station 110. Inanother example, base station 110 may send a request for subscriberstation 120 to initiate channel sounding operations by sending soundinginformation to base station 110. Base station 110 may then measure thechannel state information. The techniques used to measure channel stateinformation may vary according to a given implementation, and theembodiments are not limited in this context.

The measurements may be performed over any number of time periods. Forexample, the measurements of a channel may be performed on a per framebasis using the preambles for each frame. The channel responsesgenerated by the transmission of the preambles may be stored in memory,and indexed by frame number. In this manner, the number of measuredchannel state information may be retrieved for channel state predictionoperations.

In one embodiment, MAC 210 of node 200 may be arranged to predictchannel state information for a closed loop MIMO channel using themeasured channel state information. The measured channel stateinformation used for the prediction may cover any desired time intervalfor previous communications, such as X number of measured channel stateinformation for X previous communicated frames. The embodiments are notlimited in this context.

The channel state prediction operations may be performed by variousnodes in system 100. For example, subscriber station 120 may be arrangedto predict channel state information for some future point in time usingmeasured channel state information stored by subscriber station 120.Subscriber station 120 may then forward the predicted channel stateinformation to base station 110. In another example, base station 110may be arranged to predict channel state information for the channel ata future point in time. Base station 110 may perform the channel stateprediction operations using measured channel state information receivedfrom subscriber station 120, or as previously measured and stored bybase station 110. It may be appreciated that channel state predictionoperations may also be performed by other nodes in communication withsystem 100, not necessarily shown in FIG. 1, and still fall within thescope of the embodiments. The embodiments are not limited in thiscontext.

In one embodiment, a node of system 100 may be arranged to predictchannel state information for some future point in time using measuredchannel state information. The amount of time P used in the predictionmay vary according to the amount of feedback delay anticipated by thesystem. For example, in the case where subscriber station 120 mayreceive sounding information from base station 110, and the averageframe duration is 5 ms, the amount of anticipated feedback delay maycomprise approximately 10 ms (e.g., 5 ms for the downlink frame and 5 msfor the uplink frame). Subscriber station 120 may predict the channelstate information for P=10 ms into the future, thereby allowing for theanticipated feedback delay in the MIMO channel between base station 110and subscriber station 120. In another example, in the case where basestation 110 may instruct subscriber station 120 to perform channelsounding, the amount of anticipated feedback delay may be less than 10ms if base station 110 is performing the measurements and generating thebeamforming matrices. The amount of feedback delay and amount of timeused in the prediction may vary according to a given implementation, andthe embodiments are not limited in this context.

In one embodiment, channel state prediction operations may also beperformed in multiple stages. In a first stage, for example, subscriberstation 120 may predict channel state information for P ms into thefuture, and forward the predicted channel state information orbeamforming matrices to base station 110. In a second stage, forexample, base station 110 may receive the predicted channel stateinformation or beamforming matrices from subscriber station 120, and mayupdate the predicted channel state information before conducting theactual beamforming to account for any additional processing delay addedby base station 110. For example, base station 110 may predict thebeamforming matrix in M ms if beamforming is conducted P+M ms afterchannel sounding. Assuming P=10 ms and M=0.5 ms, for example, the totalamount of predicted time may be 10.5 ms (e.g., 10 ms+0.5 ms=10.5 ms). Itmay be appreciated that these values are used by way of example only,and the embodiments are not limited in this context.

In one embodiment, channel state prediction operations may be performedin various modes. For example, channel state prediction operations maybe performed on a periodic basis, discrete sample basis, packet ormultiple packet basis, in response to an explicit request, on acontinuous basis, and so forth. The various modes may be advantageousfor a given set of design constraints. The embodiments are not limitedin this context.

In one embodiment, for example, node 200 may be arranged to continuouslyperform channel state prediction operations. A continuous predictionmode may be particularly useful, for example, when transmitting longerbeamformed packets. Continuous prediction mode may be implemented usingregular or irregular spaced data. For example, base station 110 mayreceive channel state information periodically or not periodically. Basestation 110 may receive information at regular time intervals (e.g.,every 5 ms) or at irregular time intervals (e.g., 1, 7, 6, 3 ms). In thelatter case, channel state prediction operations can be arranged toutilize the irregular spaced data. The needed information can beobtained using sounding information in the uplink packets received froma subscriber station. Based on the sounding information, base station110 may determine that a longer packet size is appropriate for thechannel. For example, assume the packet duration is approximately 2 ms.Base station 110 can be arranged to predict the channel change for this2 ms period using the sounding information. The sounding information maycomprise discrete samples or continuous data for shorter time periods.For example, the predicted channel response can be continuously updatedfor the period or the prediction is updated on a discrete sample basis(e.g., every 0.1 ms). For an 802.16e system, updating the prediction fora discrete sample point such as 0.1 ms may be sufficient for normaloperations. Base station 110 can use the continuously updatingpredictions to send the packet to the subscriber station using abeamforming technique. The continuously updating beamforming matrix canbe applied to both data symbols and pilot symbols so that the subscriberstation can track the changes or update the beamforming matrixaccordingly. The embodiments are not limited in this context.

In one embodiment, the results of the channel prediction operations maybe measured and used to improve future channel prediction operations. Afeedback loop may be established between the various nodes of system100. The various nodes may be arranged to communicate the feedback delayor prediction period so that it improves the accuracy of futurepredictions. For example, base station 110 may communicate predictionresults to subscriber stations 120, 150. Alternatively, subscriberstations 120, 150 may report the predicted quantity with a specified(e.g., estimated) delay. The embodiments are not limited in thiscontext.

Once channel prediction operations have been performed, the predictedchannel state information may be used for a number of applications toimprove use of a communication channel. In one embodiment, for example,the predicted channel state information may be used to generate one ormore beamforming matrices for use by MIMO transceiver array 230. In oneembodiment, for example, the predicted channel state information mayalso be used to select other operating parameters for a communicationchannel. An example of the latter case may include a modulation codingscheme. The embodiments are not limited in this context.

In one embodiment, the predicted channel state information may be usedto generate one or more beamforming matrices. Transceiver array 230 mayuse the beamforming matrices to communicate information between thevarious nodes of system 100. The information may comprise, for example,one or more packets, with each packet to comprise media informationand/or control information. The media and/or control information may berepresented using, for example, multiple OFDM symbols. A packet in thiscontext may refer to any discrete set of information, including a unit,frame, cell, segment, fragment, and so forth. The packet may be of anysize suitable for a given implementation. The embodiments are notlimited in this context.

The beamforming matrices may be generated by various nodes within system100. For example, subscriber station 120 may generate the beamformingmatrices using the predicted channel state information stored bysubscriber station 120 or received from another node of system 100.Subscriber station 120 may forward the beamforming matrices to basestation 110. In another example, base station 110 may generate thebeamforming matrices using the predicted channel state informationstored by base station 110 or received from another node of system 100(e.g., subscriber stations 120, 150). Transmitting the beamformingmatrices instead of the predicted channel state information may consumeless bandwidth in some applications. The embodiments, however, are notlimited in this context.

In addition to using the predicted channel state information forbeamforming, the predicted channel state information may also be used toselect other operating parameters for a communication channel. Forexample, the predicted channel state information may be used to select amodulation coding scheme. In an 802.16 system, a subscriber station mayalso provide feedback regarding the type of modulation coding schemethat is appropriate for a given communications channel based on measuredchannel state information. The various parameters of a modulation codingscheme may include, for example, a modulation level, a code rate, aspatial multiplex mode, a diversity mode, and so forth. The subscriberstation may suggest or recommend a modulation coding scheme, or asetting for a parameter of a modulation coding scheme, for the nextpacket based on the measured channel state information of a givenchannel. MAC 210 may use the predicted channel state information asinput for the modulation coding scheme selection algorithm to provide abetter modulation coding scheme for a given moment in time. Theembodiments are not limited in this context.

Furthermore, the predicted channel state information can be used for thecomputation of any quantity that uses channel state information as oneof the input arguments. For example, the CINR feedback information maycomprise the signal to noise plus interference ratio (SINR) of eachspatial stream at the output of the spatial de-multiplexer at thereceiver. The spatial demultiplexer may be arranged to perform minimummean square error (MMSE) spatial decoupling. The quantity may depend onchannel state information, noise level, transmission scheme, and spatialdemultiplexer technique. The SINR computation can employ the predictedchannel state information in both estimating the future received signallevel and the computation of the spatial de-multiplexer (e.g., MMSEfilter weights). The predicted SINR may be fed back instead of the SINRcomputed from the current channel state information. In addition to MMSEspatial decoupling, the predicted channel state information may be usedto perform maximum likelihood spatial decoupling, successivecancellation decoupling, and so forth. The embodiments are not limitedin this context.

It is worthy to note that although the predicted channel stateinformation is described for use with various applications as describedabove, it may be appreciated that the predicted channel stateinformation may be suitable for other applications, such as configuringvarious other operating parameters for a 802.16 system that are based inwhole or in part upon channel state information for a givencommunications channel. The embodiments are not limited in this context.

The actual channel prediction may be performed in a number of differentways. For example, predicting channel state information may be performedusing a filter. The filter may be implemented using hardware, software,or a combination of both. In one embodiment, for example, the filter maybe a software filter executed by MAC 210 of node 200. The filter maycomprise a finite impulse response (FIR) filter having multiple filtertaps 1−N, where N may represent any positive integer. Each filter tapmay represent a FIR coefficient/delay pair. In one embodiment, forexample, the FIR filter may be implemented using a 5-tap filter (e.g.,N=5). In this example, the input to the 5-tap filter may comprise 5previous channel responses estimated from the preambles of the previous5 frames. The filter is derived with low complexities from 5autocorrelation coefficients of the fading process, which may becomputed from previous preambles. Simulation results demonstrate thatthe varying channel can be predicted using a 5-tap FIR filter with errorrates of 20% or less. With this prediction technique, approximately 2-4dB gain can be obtained over a system without channel prediction. Thenumber of filter taps may vary according to a given set of designconstraints, and the embodiments are not limited in this context.

In one embodiment, the filter may comprise a linear one-step predictor,which is essentially a special kind of Wiener-Hopf filter. Using a moresophisticated estimator such as a Multiple Signal Classification (MUSIC)filter may be advantageous for implementations anticipating vehiclespeeds of 10 km/h or more. The filter types may vary according to agiven set of design constraints, and the embodiments are not limited inthis context.

FIG. 4 illustrates a graph of a fading magnitude of a predicting filterin accordance with one embodiment. FIG. 4 illustrates the performance ofa 5-tap filter used in channel prediction operations. As shown by FIG.4, the fading magnitude of a channel closely matches the predictedfading magnitude using the 5-tap filter. In fact, FIG. 4 illustratesthat the error rates between predicted channel state information andactual channel state information is approximately 20% or less using the5-tap filter.

FIG. 5 illustrates a graph of packet error rates (PER) for systems withand without channel prediction in accordance with one embodiment. FIG. 5illustrates the PER relative to the signal-to-noise ratio (SNR) for asystem using channel prediction and for a system not using channelprediction. Assuming a feedback delay of 10 ms, FIG. 5 shows that thePER for a system using channel prediction is significantly lower for thesame SNR relative to a system without channel prediction.

FIG. 6 illustrates a graph of throughputs for systems with and withoutchannel prediction in accordance with one embodiment. FIG. 6 illustratesthe throughput in Mega-bits-per-second (Mbps) relative to the SNR for asystem using channel prediction and for a system not using channelprediction. Assuming a feedback delay of 10 ms again, FIG. 6 shows thatthe throughput for a system using channel prediction is significantlyhigher for the same SNR relative to a system without channel prediction.

Operations for the above embodiments may be further described withreference to the following figures and accompanying examples. Some ofthe figures may include programming logic. Although such figurespresented herein may include a particular programming logic, it can beappreciated that the programming logic merely provides an example of howthe general functionality described herein can be implemented. Further,the given programming logic does not necessarily have to be executed inthe order presented unless otherwise indicated. In addition, the givenprogramming logic may be implemented by a hardware element, a softwareelement executed by a processor, or any combination thereof. Theembodiments are not limited in this context.

FIG. 7 illustrates a programming logic in accordance with oneembodiment. Programming logic 700 may be representative of theoperations executed by one or more systems described herein, such asnode 200 as implemented as part of base station 110 or subscriberstations 120, 150, for example. As shown in programming logic 700, atime interval for channel prediction may be selected at block 702. Thetime interval may comprise, for example, a feedback delay intervalanticipated for a given system. Channel state information for a closedloop MIMO channel may be predicted using the time interval and measuredchannel state information at block 704.

In one embodiment, information may be transmitted over the closed loopMIMO communications channel using the predicted channel stateinformation. The information may comprise, for example, a packet havingone or more OFDM symbols. The information may be transmitted by a MIMOtransceiver array having multiple antennas. The embodiments are notlimited in this context.

In one embodiment, the predicted channel state information may betransmitted over the MIMO communications channel. This may occur, forexample, if a subscriber station performs the channel prediction, andthe subscriber station sends the predicted channel state information toanother device, such as a base station. The embodiments are not limitedin this context.

In one embodiment, a beamforming matrix may be generated using thepredicted channel state information. For example, the MIMO transceiverarray may use a beamforming matrix to communicate the information. Thebeamforming matrix may be used, for example, to control the direction inwhich the information is to be transmitted by the MIMO transceiverarray. The beamforming matrix may be generated by any number of devicesin the system, such as base station 110 and/or subscriber stations 120,150, for example. The embodiments are not limited in this context.

In one embodiment, a beamforming matrix generated using the predictedchannel state information may be transmitted over the MIMOcommunications channel. This may occur, for example, if a subscriberstation generates the beamforming matrix, and the subscriber stationsends the beamforming matrix to another device, such as a base station.The embodiments are not limited in this context.

It should be understood that the embodiments may be used in a variety ofapplications. As described above, the circuits and techniques disclosedherein may be used in many apparatuses such as transmitters andreceivers of a radio system. Transmitters and/or receivers intended tobe included within the scope of the embodiments may include, by way ofexample only, WLAN transmitters and/or receivers, MIMOtransmitters-receivers system, two-way radio transmitters and/orreceivers, digital system transmitters and/or receivers, analog systemtransmitters and/or receivers, cellular radiotelephone transmittersand/or receivers, and so forth. The embodiments are not limited in thiscontext.

Types of WLAN transmitters and/or receivers intended to be within thescope of the embodiments may include, although are not limited to,transmitters and/or receivers for transmitting and/or receiving spreadspectrum signals such as, for example, Frequency Hopping Spread Spectrum(FHSS), Direct Sequence Spread Spectrum (DSSS) OFDM transmitters and/orreceivers, and so forth. The embodiments are not limited in thiscontext.

Numerous specific details have been set forth herein to provide athorough understanding of the embodiments. It will be understood bythose skilled in the art, however, that the embodiments may be practicedwithout these specific details. In other instances, well-knownoperations, components and circuits have not been described in detail soas not to obscure the embodiments. It can be appreciated that thespecific structural and functional details disclosed herein may berepresentative and do not necessarily limit the scope of theembodiments.

It is also worthy to note that any reference to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. The appearances of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment.

Some embodiments may be implemented using an architecture that may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherperformance constraints. For example, an embodiment may be implementedusing software executed by a general-purpose or special-purposeprocessor. In another example, an embodiment may be implemented asdedicated hardware, such as a circuit, an application specificintegrated circuit (ASIC), Programmable Logic Device (PLD) or digitalsignal processor (DSP), and so forth. In yet another example, anembodiment may be implemented by any combination of programmedgeneral-purpose computer components and custom hardware components. Theembodiments are not limited in this context.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other. Theembodiments are not limited in this context.

Some embodiments may be implemented, for example, using amachine-readable medium or article which may store an instruction or aset of instructions that, if executed by a machine, may cause themachine to perform a method and/or operations in accordance with theembodiments. Such a machine may include, for example, any suitableprocessing platform, computing platform, computing device, processingdevice, computing system, processing system, computer, processor, or thelike, and may be implemented using any suitable combination of hardwareand/or software. The machine-readable medium or article may include, forexample, any suitable type of memory unit, such as the examples givenwith reference to FIG. 2. For example, the memory unit may include anymemory device, memory article, memory medium, storage device, storagearticle, storage medium and/or storage unit, memory, removable ornon-removable media, erasable or non-erasable media, writeable orre-writeable media, digital or analog media, hard disk, floppy disk,Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R),Compact Disk Rewriteable (CD-RW), optical disk, magnetic media, varioustypes of Digital Versatile Disk (DVD), a tape, a cassette, or the like.The instructions may include any suitable type of code, such as sourcecode, compiled code, interpreted code, executable code, static code,dynamic code, and the like. The instructions may be implemented usingany suitable high-level, low-level, object-oriented, visual, compiledand/or interpreted programming language, such as C, C++, Java, BASIC,Perl, Matlab, Pascal, Visual BASIC, assembly language, machine code, andso forth. The embodiments are not limited in this context.

While certain features of the embodiments have been illustrated asdescribed herein, many modifications, substitutions, changes andequivalents will now occur to those skilled in the art. It is thereforeto be understood that the appended claims are intended to cover all suchmodifications and changes as fall within the true spirit of theembodiments.

1-25. (canceled)
 26. An apparatus, comprising a media access controlprocessor to perform a sounding of a closed-loopmultiple-input-multiple-output (MIMO) communication channel, receive abeamforming matrix in response to the sounding, update the receivedbeamforming matrix to account for a processing delay, and conductbeamforming using the updated beamforming matrix.
 27. The apparatus ofclaim 26, the received beamforming matrix generated based on measuredchannel state information for the closed-loop MIMO communicationchannel.
 28. The apparatus of claim 26, the received beamforming matrixgenerated based on an anticipated feedback delay associated with thesounding of the closed-loop MIMO communication channel.
 29. Theapparatus of claim 26, the received beamforming matrix generated basedon predicted channel state information for a first future point in time,the media access control processor to determine a second future point intime based on the first future point in time and the processing delayand update the received beamforming matrix for use in beamforming at thesecond future point in time
 30. The apparatus of claim 29, the mediaaccess control processor to determine updated predicted channel stateinformation based on the predicted channel state information and theprocessing delay and update the received beamforming matrix based on theupdated predicted channel state information.
 31. The apparatus of claim30, the media access control processor to select a modulation and codingscheme for the closed-loop MIMO communication channel using the updatedpredicted channel state information.
 32. The apparatus of claim 30, themedia access control processor to perform minimum mean square error(MMSE) spatial decoupling using the updated predicted channel stateinformation.
 33. The apparatus of claim 26, the measured channel stateinformation to comprise channel responses for previously communicatedframes of information.
 34. The apparatus of claim 26, comprising a MIMOtransceiver array to transmit one or more orthogonal frequency divisionmultiplexing (OFDM) symbols over the closed-loop MIMO communicationchannel.
 35. At least one non-transitory machine-readable storage mediumstoring a set of instructions that, in response to being executed by aprocessor, enable a system to: perform a sounding of a closed-loopmultiple-input-multiple-output (MIMO) communication channel; receive abeamforming matrix in response to the sounding; update the receivedbeamforming matrix to account for a processing delay; and conductbeamforming using the updated beamforming matrix.
 36. The at least onenon-transitory machine-readable storage medium of claim 35, the receivedbeamforming matrix generated based on measured channel state informationfor the closed-loop MIMO communication channel.
 37. The at least onenon-transitory machine-readable storage medium of claim 35, the receivedbeamforming matrix generated based on an anticipated feedback delayassociated with the sounding of the closed-loop MIMO communicationchannel.
 38. The at least one non-transitory machine-readable storagemedium of claim 35, storing instructions that, in response to beingexecuted by the processor, enable the system to: determine a secondfuture point in time based on a first future point in time and theprocessing delay, the received beamforming matrix generated based onpredicted channel state information for the first future point in time;and update the received beamforming matrix for use in beamforming at thesecond future point in time.
 39. The at least one non-transitorymachine-readable storage medium of claim 38, storing instructions that,in response to being executed by the processor, enable the system to:determine updated predicted channel state information based on thepredicted channel state information and the processing delay; and updatethe received beamforming matrix based on the updated predicted channelstate information.
 40. The at least one non-transitory machine-readablestorage medium of claim 39, storing instructions that, in response tobeing executed by the processor, enable the system to select amodulation and coding scheme for the closed-loop MIMO communicationchannel using the updated predicted channel state information.
 41. Theat least one non-transitory machine-readable storage medium of claim 39,storing instructions that, in response to being executed by theprocessor, enable the system to perform minimum mean square error (MMSE)spatial decoupling using the updated predicted channel stateinformation.
 42. The at least one non-transitory machine-readablestorage medium of claim 35, the measured channel state information tocomprise channel responses for previously communicated frames ofinformation.
 43. A method, comprising: performing, by a media accesscontrol processor, a sounding of a closed-loopmultiple-input-multiple-output (MIMO) communication channel; receiving abeamforming matrix in response to the sounding; updating the receivedbeamforming matrix to account for a processing delay; and conductingbeamforming using the updated beamforming matrix.
 44. The method ofclaim 43, the received beamforming matrix generated based on measuredchannel state information for the closed-loop MIMO communicationchannel.
 45. The method of claim 43, the received beamforming matrixgenerated based on an anticipated feedback delay associated with thesounding of the closed-loop MIMO communication channel.
 46. The methodof claim 43, comprising: determining a second future point in time basedon a first future point in time and the processing delay, the receivedbeamforming matrix generated based on predicted channel stateinformation for the first future point in time; and updating thereceived beamforming matrix for use in beamforming at the second futurepoint in time.
 47. The method of claim 46, comprising: determiningupdated predicted channel state information based on the predictedchannel state information and the processing delay; and updating thereceived beamforming matrix based on the updated predicted channel stateinformation.
 48. The method of claim 47, comprising selecting amodulation and coding scheme for the closed-loop MIMO communicationchannel using the updated predicted channel state information.
 49. Themethod of claim 47, comprising performing minimum mean square error(MMSE) spatial decoupling using the updated predicted channel stateinformation.
 50. The method of claim 43, the measured channel stateinformation to comprise channel responses for previously communicatedframes of information.